Application launch support

ABSTRACT

A method of software launch regression testing comprises monitoring a user engagement parameter of an existing application running on a plurality of client devices and determining a probability interval from the user engagement parameter of the existing application. A candidate update application is then launched to a subset of the plurality of client devices. The method then proceeds with monitoring a corresponding user engagement parameter of the candidate update version running on the subset of client devices, determining if the corresponding user engagement parameter of the candidate update version falls within the probability interval, and, based on the probability interval falling within the probability interval, providing a testing pass notification.

RELATED APPLICATION DATA

This application is a continuation of U.S. patent application Ser. No.17/490,858 filed on Sep. 30, 2021, the contents of which areincorporated herein by reference as if explicitly set forth.

BACKGROUND

Regression testing of software is done when changes are made to anexisting software application or when there is an update of related codewith which the existing software application interacts, for example anoperating system update. The goal of regression testing is to check thatsuch changes do not affect the current features of the softwareapplication, that is, that the capabilities of the existing softwareapplication do not regress on launch of the updated version or whenrunning with the new associated code. One method of performingregression testing is to re-run previous automated tests on the new code(or to re-run the previous automated tests on the old code running onthe new operating system) and compare the automated test results for thenew code versus the automated test results for the old code. One of thebenefits of such regression testing is that the automated tests alreadyexist, but a disadvantage is that the effectiveness of the automatedtests depend on the ability of the people creating the tests to providea comprehensive suite of tests.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some nonlimiting examples areillustrated in the figures of the accompanying drawings in which:

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexamples.

FIG. 2 is a diagrammatic representation of a messaging system, inaccordance with some examples, that has both client-side and server-sidefunctionality.

FIG. 3 is a graph showing a hypothetical metric (M) depicted over timefor candidate software update adopters and non-adopters, before andafter partial launch of the candidate software update, in one example.

FIG. 4 is a graph of a content creation metric before and after partiallaunch of candidate software update, and associated intervals determinedtherefrom, in one example.

FIG. 5 is a table showing various metrics and their suitability for usein the regression testing methods disclosed herein, in one example.

FIG. 6 is a flowchart 600 illustrating a regression testing methodaccording to one example.

FIG. 7 is a flowchart 700 illustrating a regression testing methodaccording to one example.

FIG. 8 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples.

FIG. 9 is a block diagram showing a software architecture within whichexamples may be implemented.

DETAILED DESCRIPTION

In some examples, regression testing is performed by introducing acandidate update to a software application to a subset (for example1%-30%) of the user-base of the existing software application (known asa “staging rollout”), to detect changes to user engagement from previousreleases. This method of regression testing is typically better thanusing “beta testers,” as beta testers are self-selected (typically powerusers) and their use may not be predictive of how the entire userpopulation will react to or interact with the new application version.This method of regression testing may also be superior to qualityassurance engineers writing automated tests, as it is difficult toconceive of every edge case, or simulate the variability of users'environments (network connectivity, device performance,language-specific changes and so forth). Also, re-running previousautomated tests does not account for user behavioral changes over time,which can bias the results achieved from such automated tests.

Though traditional regression testing works well at the micro level,even perfectly implemented software may have undesired side-effects whenused in an intricate system, for example in a social network. To testfor macro side effects, the methods described herein mimic an A/B testby observing changes to metrics between adopters of the change (i.e.,users who update the app to the latest version during staging rollout)vs non-adopters, or by comparing before-and-after-adoption metrics foradopters. By observing changes to metrics such as Daily Active Users(DAU), Time Spent, content creation frequency, messaging frequency, andso forth, for the initial adopters, engagement can be determined to be“normal,” which can signal full rollout of the update. Alternatively,the rollout of the update may be aborted in the case of abnormalengagement.

In one example, a method of software launch regression testing,performed by one or more processors, includes capturing an operationalparameter of an existing application running on a plurality of clientdevices, determining a probability interval from the operationalparameter of the existing application, launching a candidate updateapplication to a subset of the plurality of client devices, monitoring acorresponding operational parameter of the candidate update applicationrunning on the subset of the plurality of client devices, determining ifthe corresponding operational parameter of the candidate updateapplication falls within the probability interval, and based on thecorresponding operational parameter falling within the probabilityinterval, providing a testing pass notification.

The size of the probability interval may depend on whether or not theoperation parameter is a critical parameter. The probability intervalmay comprise a confidence interval within which correct functioning ofthe candidate update application can be assumed, and the method mayfurther include determining a prediction interval from the operationalparameter of the existing application, the prediction intervalcomprising an interval of the operational parameter within which thecandidate update application may or may not be functioning correctly,determining if the corresponding operational parameter of the candidateupdate application falls within the prediction interval, and based onthe corresponding operational parameter falling outside the confidenceinterval but within the prediction interval, providing a testing reviewnotification. Based on the corresponding operational parameter fallingoutside the prediction interval, a testing fail notification may beprovided.

Launching of the candidate update application may include uploading thecandidate update application to an app store, where a number ofavailable downloads of the candidate update application from the appstore is limited to a predetermined number. Launching of the candidateupdate application may also include limiting launch of the candidateupdate application to client devices having a particular characteristic.The particular characteristic may be location of the client deviceswithin a particular geographical area or time zone.

In one example, the operational parameter is a ratio of transmissions ofcontent from the subset of the plurality of client devices to successfulviewing of the content on recipient client devices.

In another example, provided is a non-transitory computer-readablestorage medium, the computer-readable storage medium includinginstructions that when executed by a computer, cause the computer toperform operations for software launch regression testing, including butnot limited to the methods described above. For example, the operationsmay comprise capturing an operational parameter of an existingapplication running on a plurality of client devices, determining aprobability interval from the operational parameter of the existingapplication, launching a candidate update application to a subset of theplurality of client devices, monitoring a corresponding operationalparameter of the candidate update application running on the subset ofthe plurality of client devices, determining if the correspondingoperational parameter of the candidate update application falls withinthe probability interval, and based on the corresponding operationalparameter falling within the probability interval, providing a testingpass notification.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

Networked Computing Environment

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102, each of which hosts a number of applications, including a messagingclient 104 and other applications 106. For the purpose of illustratingthe regression testing methods by way of example, the methods aredescribed herein with reference to an update of the software comprisingthe messaging client 104, or an update to the operating system of theclient device 102 on which the messaging client 104 runs.

Each messaging client 104 is communicatively coupled to other instancesof the messaging client 104 (e.g., hosted on respective other clientdevices 102) and a messaging server system 108 via a network 112 (e.g.,the Internet). A messaging client 104 can also communicate withlocally-hosted applications 106 using Application Program Interfaces(APIs). The messaging system 100 also includes one or more developer/QAdevices 110, which are communicatively coupled to the messaging serversystem 108 via the network 112. The developer/QA devices 110 provide ameans whereby software engineers or software release personnel canadminister the partial release of updated software for the messagingclient 104 or another application 106.

A messaging client 104 is able to communicate and exchange data withother messaging clients 104 and with the messaging server system 108 viathe network 112. The data exchanged between messaging clients 104, andbetween a messaging client 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 112 to a particular messaging client 104. While certainfunctions of the messaging system 100 are described herein as beingperformed by either a messaging client 104 or by the messaging serversystem 108, the location of certain functionality either within themessaging client 104 or the messaging server system 108 may be a designchoice. For example, it may be technically preferable to initiallydeploy certain technology and functionality within the messaging serversystem 108 but to later migrate this technology and functionality to themessaging client 104 where a client device 102 has sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client 104. Such operations includetransmitting data to, receiving data from, and processing data generatedby the messaging client 104. This data may include message content,client device information, geolocation information, media augmentationand overlays, message content persistence conditions, social networkinformation, and live event information, as examples. Data exchangeswithin the messaging system 100 are invoked and controlled throughfunctions available via user interfaces (UIs) of the messaging client104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 116 is coupled to, andprovides a programmatic interface to, application servers 114. Theapplication servers 114 are communicatively coupled to a database server120, which facilitates access to a database 126 that stores dataassociated with messages processed by the application servers 114.Similarly, a web server 128 is coupled to the application servers 114,and provides web-based interfaces to the application servers 114. Tothis end, the web server 128 processes incoming network requests overthe Hypertext Transfer Protocol (HTTP) and several other relatedprotocols.

The Application Program Interface (API) server 116 receives andtransmits message data (e.g., commands and message payloads) between theclient device 102 and the application servers 114. Specifically, theApplication Program Interface (API) server 116 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client 104 in order to invoke functionality of theapplication servers 114. The Application Program Interface (API) server116 exposes various functions supported by the application servers 114,including account registration, login functionality, the sending ofmessages, via the application servers 114, from a particular messagingclient 104 to another messaging client 104, the sending of media files(e.g., images or video) from a messaging client 104 to a messagingserver 118, and for possible access by another messaging client 104, thesettings of a collection of media data (e.g., story), the retrieval of alist of friends of a user of a client device 102, the retrieval of suchcollections, the retrieval of messages and content, the addition anddeletion of entities (e.g., friends) to an entity graph (e.g., a socialgraph), the location of friends within a social graph, and opening anapplication event (e.g., relating to the messaging client 104).

The application servers 114 host a number of server applications andsubsystems, including for example a messaging server 118, an imageprocessing server 122, and a social network server 124. The messagingserver 118 implements a number of message processing technologies andfunctions, particularly related to the aggregation and other processingof content (e.g., textual and multimedia content) included in messagesreceived from multiple instances of the messaging client 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available to themessaging client 104. Other processor and memory intensive processing ofdata may also be performed server-side by the messaging server 118, inview of the hardware requirements for such processing.

The application servers 114 also include an image processing server 122that is dedicated to performing various image processing operations,typically with respect to images or video within the payload of amessage sent from or received at the messaging server 118.

The social network server 124 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server 118. To this end, the social network server 124maintains and accesses an entity graph within the database 126. Examplesof functions and services supported by the social network server 124include the identification of other users of the messaging system 100with which a particular user has relationships or is “following,” andalso the identification of other entities and interests of a particularuser.

The messaging client 104 can notify a user of the client device 102, orother users related to such a user (e.g., “friends”), of activity takingplace in one or more external resources, for example games or slimmeddown versions of third party applications. For example, the messagingclient 104 can provide participants in a conversation (e.g., a chatsession) in the messaging client 104 with notifications relating to thecurrent or recent use of an external resource by one or more members ofa group of users. One or more users can be invited to join in an activeexternal resource or to launch a recently-used but currently inactive(in the group of friends) external resource. The external resource canprovide participants in a conversation, each using respective messagingclients 104, with the ability to share an item, status, state, orlocation in an external resource with one or more members of a group ofusers into a chat session. The shared item may be an interactive chatcard with which members of the chat can interact, for example, to launchthe corresponding external resource, view specific information withinthe external resource, or take the member of the chat to a specificlocation or state within the external resource. Within a given externalresource, response messages can be sent to users on the messaging client104. The external resource can selectively include different media itemsin the responses, based on a current context of the external resource.

The messaging system 100 in the illustrated example includes anapplication launch-support server 130 as part of messaging server system108, although application launch-support server 130 may alternative beprovided as part of a separate software development or software releaseserver system. The application launch-support server 130 receives andstores candidate update software for the messaging client 104 (or otherapplications) from one or more developer/QA devices 110. The applicationlaunch-support server 130 then uploads the candidate update software toa limited set of client devices 102 or to an app store with a limitationon the number of possible downloads from the app store. The applicationlaunch-support server 130 then monitors the user engagement with themessaging client 104 before and after the deployment of the candidateupdate software, and determines whether the user engagement is or isn'twithin parameters indicating that it is a candidate for a full releaseof the candidate update software as a final software update. Dependingon the particular determinations, appropriate reports, messages or othernotifications are provided by the application launch-support server 130to the developer/QA devices 110. The methods implemented by theapplication launch-support server 130 are described in more detailbelow.

System Architecture

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to some examples. Specifically, themessaging system 100 is shown to comprise the messaging client 104 andthe application servers 114. The messaging system 100 embodies a numberof subsystems, which are supported on the client-side by the messagingclient 104 and on the sever-side by the application servers 114. Thesesubsystems include, for example, an ephemeral timer system 202, acollection management system 204, an augmentation system 208, a mapsystem 210, a game system 212, and an interaction reporting system 214.

The ephemeral timer system 202 is responsible for enforcing thetemporary or time-limited access to content by the messaging client 104and the messaging server 118. The ephemeral timer system 202incorporates a number of timers that, based on duration and displayparameters associated with a message, or collection of messages (e.g., astory), selectively enable access (e.g., for presentation and display)to messages and associated content via the messaging client 104. Furtherdetails regarding the operation of the ephemeral timer system 202 areprovided below.

The collection management system 204 is responsible for managing sets orcollections of media (e.g., collections of text, image video, and audiodata). A collection of content (e.g., messages, including images, video,text, and audio) may be organized into an “event gallery” or an “eventstory.” Such a collection may be made available for a specified timeperiod, such as the duration of an event to which the content relates.For example, content relating to a music concert may be made availableas a “story” for the duration of that music concert. The collectionmanagement system 204 may also be responsible for publishing an iconthat provides notification of the existence of a particular collectionto the user interface of the messaging client 104.

The collection management system 204 furthermore includes a curationinterface 206 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface206 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certain examples,compensation may be paid to a user for the inclusion of user-generatedcontent into a collection. In such cases, the collection managementsystem 204 operates to automatically make payments to such users for theuse of their content.

The augmentation system 208 provides various functions that enable auser to augment (e.g., annotate or otherwise modify or edit) mediacontent associated with a message. For example, the augmentation system208 provides functions related to the generation and publishing of mediaoverlays for messages processed by the messaging system 100. Theaugmentation system 208 operatively supplies a media overlay oraugmentation (e.g., an image filter) to the messaging client 104 basedon a geolocation of the client device 102. In another example, theaugmentation system 208 operatively supplies a media overlay to themessaging client 104 based on other information, such as social networkinformation of the user of the client device 102. A media overlay mayinclude audio and visual content and visual effects. Examples of audioand visual content include pictures, texts, logos, animations, and soundeffects. An example of a visual effect includes color overlaying. Theaudio and visual content or the visual effects can be applied to a mediacontent item (e.g., a photo) at the client device 102. For example, themedia overlay may include text or image that can be overlaid on top of aphotograph taken by the client device 102. In another example, the mediaoverlay includes an identification of a location overlay (e.g., Venicebeach), a name of a live event, or a name of a merchant overlay (e.g.,Beach Coffee House). In another example, the augmentation system 208uses the geolocation of the client device 102 to identify a mediaoverlay that includes the name of a merchant at the geolocation of theclient device 102. The media overlay may include other indiciaassociated with the merchant. The media overlays may be stored in thedatabase 126 and accessed through the database server 120.

In some examples, the augmentation system 208 provides a user-basedpublication platform that enables users to select a geolocation on a mapand upload content associated with the selected geolocation. The usermay also specify circumstances under which a particular media overlayshould be offered to other users. The augmentation system 208 generatesa media overlay that includes the uploaded content and associates theuploaded content with the selected geolocation.

The map system 210 provides various geographic location functions, andsupports the presentation of map-based media content and messages by themessaging client 104. For example, the map system 210 enables thedisplay of user icons or avatars on a map to indicate a current or pastlocation of “friends” of a user, as well as media content (e.g.,collections of messages including photographs and videos) generated bysuch friends, within the context of a map. For example, a message postedby a user to the messaging system 100 from a specific geographiclocation may be displayed within the context of a map at that particularlocation to “friends” of a specific user on a map interface of themessaging client 104. A user can furthermore share his or her locationand status information (e.g., using an appropriate status avatar) withother users of the messaging system 100 via the messaging client 104,with this location and status information being similarly displayedwithin the context of a map interface of the messaging client 104 toselected users.

The game system 212 provides various gaming functions within the contextof the messaging client 104. The messaging client 104 provides a gameinterface providing a list of available games that can be launched by auser within the context of the messaging client 104, and played withother users of the messaging system 100. The messaging system 100further enables a particular user to invite other users to participatein the play of a specific game, by issuing invitations to such otherusers from the messaging client 104. The messaging client 104 alsosupports both the voice and text messaging (e.g., chats) within thecontext of gameplay, provides a leaderboard for the games, and alsosupports the provision of in-game rewards (e.g., coins and items).

The interaction reporting system 214 provides tracking and reporting ofuser interactions with the messaging client 104. In most cases thisoccurs inherently during the operation of the messaging client 104 aspart of its functioning. This data collection and reporting may be topermit relevant commands to be executed by the messaging client 104 orthe application servers 114. Additionally, user interactions and relatedengagement metrics may already be being observed or determined in theordinary course of operation for reasons other than regression testing,such as to verify correct functioning, to log exposure to commercialmessages or pay for content or for referrals, to update social networkentity and relationship graphs, and so forth. The data collectionrequired for operation of a system such as messaging system 100 or othersocial networking system provides the further benefit that metrics ofexisting messaging clients 104 are available and can be analyzed beforethe launch of any candidate update software, without requiringadditional modifications to the messaging client 104 to do so.

FIG. 3 is a graph showing a hypothetical metric (M) depicted over timefor candidate software update adopters and non-adopters, before andafter partial launch of the candidate software update. The x-axis istime, starting at some initial time t(0) and ending at t(signoff), withlaunch of the candidate software update to the adopters' client devices102 occurring at t(rollout). Two rows are shown—the upper row shows aplot of the metric M over time for adopters' client devices 102 and thelower row shows a plot of the metric M over time for non-adopters'client devices 102. In one example, the non-adopters are 99% of the userpopulation of the messaging client 104 while the adopters are 1% of theuser population. Initially, from time t(0) to time t(rollout) neitherthe adopters nor the non-adopters' client devices 102 have the newversion yet, indicated by quadrants 1 and 2. Note that due to the smallsample size, the variation of the metric M for non-adopters likely willnot match the variation of M for adopters, despite both groups using thesame version of the software on their client devices 102. At a minimum,the variability of M is likely to be greater for the smaller user samplesize, as illustrated.

The candidate software update is then released out at time t(rollout).The candidate software update is released to the adopters' clientdevices 102 only, whose postlaunch data for M is indicated by quadrant3. Meanwhile, M proceeds as usual for the non-adopters, indicated byquadrant 4.

It is then determined whether or not the variation in M in quadrant 3for the adopters' client devices 102 is what is expected. This is doneby comparing M in quadrant 3 with a baseline for what is normal anddetermining whether any variation from the baseline is beyondexpectations. If the variation is beyond expectations, further analysisand remediation is performed on the candidate software update and theprocess is repeated until any variation in M is within expectations. Attime t(signoff) after final approval, the candidate software update (nowan actual software update) is released to the non-adopters' clientdevices 102 as well and the entire population has the software update.

Determining a proper baseline for what is normal has a number ofchallenges, since there are many factors other than a problem with thecandidate software update itself that can affect the variation of Mafter t(rollout). For example, causal effects. Adopter user engagementwith an existing application feature might be affected by unrelatedsystem crashes or data losses, or because users' attention has beendrawn away from an existing application feature by a new applicationfeature that is included in the candidate software update. Additionally,there may be unrelated temporal bias. If a candidate software update isreleased late on a Friday night, Saturday morning user engagement may bedifferent from weekday morning user engagement due to different lifehabits and not because of a problem with the candidate software update.

Furthermore, there may be an unrelated change in user behavior, forexample in response to negative or positive publicity, or the launch ofa competing product. Additionally, there may be a change in the baselineuser behavior, for example if the candidate software update fixes a bugin the existing software, resulting in improved functionality. Finally,the receipt of metric data may be delayed for some reason.

One example of determining whether or not M is normal after release ofthe candidate software update is to compare absolute values of thebehavior of the metric across a number of releases. If, for the mostrecent time interval, the metric value or metric behavior for the newversion falls within a range of values or behaviors for past releases,it is deemed normal. This method may be improved by using the pre-launchmetric value as well. In one example, a (post-pre) regression isdetermined over the past release values, to provide confidence andprediction intervals for the value of the metric post release.

FIG. 4 is a graph that shows a content creation metric before and afterpartial launch of previous software updates, and associated intervalsdetermined therefrom. The x-axis 404 of the graph shows the number ofitems of content created per user before launch of a software update.The y-axis 402 shows the same content creation metric after the launchof a software update.

Past launch data points 406 are illustrated by circles. These representdata for all users for past software updates, both adopters and proposednon-adopters for the candidate software update. From these data points,a fitted curve 408 can be determined, as well as a confidence interval410 and a prediction interval 412. The confidence interval 410 and theprediction interval 412 are determined by performing a regressionanalysis on post-launch and pre-launch metric values from previoussoftware update releases, i.e. on the past launch data points 406.

The prediction interval 412 in this example represents an intervalwithin which, with 95% confidence, new release data points are likely tofall if there are no errors in the candidate software update. Theconfidence interval 410 in this example represents an interval withinwhich, with 95% confidence, new release data points are likely to fallif there are probably no errors in the candidate software update. Alsoillustrated is an example of a current launch data point 414, which isgenerated from pre-launch and post-launch use of the candidate softwareupdate by adopters. Example current launch data point 414 is shown asfalling outside confidence interval 410 but within prediction interval412. In the case of such launch data points, which fall outsideconfidence interval 410 but within prediction interval 412, there may ormay not be errors in the candidate software update, but it cannot bedetermined with reasonable certainty either way.

The confidence interval 410 and prediction interval 412 can be used indifferent ways depending on the implementation. In one example, metricsare assigned tiers based on how critical they are deemed to be. Sinceconfidence intervals are narrower than prediction intervals (whichresult in more sensitive checks), confidence intervals are used to flaganomalies for metrics that are more critical while prediction intervalsare used for metrics that are less critical.

In another example, a metric is assessed against both a confidenceinterval and a prediction interval. In such a case, there are threepossible outcomes for a current launch data point 414. If it fallswithin the 95% confidence interval 410, the candidate software update isdeemed to have passed regression testing for this metric. If it isoutside the confidence interval 410 but within the looser predictioninterval 412, then it is flagged for further analysis to determine ifthere may be unrelated factors (temporal biases, etc.) that are causingthe current launch data point 414 to fall outside the confidenceinterval 410. If an unrelated factor is determined to be responsible orlikely to be responsible, the candidate software update is deemed tohave passed regression testing for this metric. Finally, if the currentlaunch data point 414 is outside the prediction interval 412, then thecandidate software update has failed the regression testing and a fulllaunch of the candidate software update is held or aborted until theproblem is resolved. By using a regression confidence interval insteadof a strict range check, there is a better chance that cases that mightbe outside a strict range but that may actually be normal are identifiedas such.

The confidence interval 410 and the prediction interval 412 may also beappropriately wider or tighter based on the density of (release) datapoints nearby. This can be seen in FIG. 4 , where both the confidenceinterval 410 (more prominently) and the prediction interval 412 pinchinward towards the center of the graph. With more accurate boundaries,this reduces the likelihood of both false positives and false negativesoccurring.

Confidence and prediction intervals and determining same from availabledata are known in the field of statistical hypothesis testing.

The nature of the metric M that is monitored also affects theperformance of the regression testing system. Preferably, the metric Mis a sensitive indicator for the overall health of the product but isless sensitive to factors that might affect the metric withoutindicating a problem with the candidate software update. That it,preferably it is stable over time and does not change markedly overweekdays, weekend days, public holidays, time of day and so forth. Alsoit is preferably stable if the user persona or device class compositionchanges. For example, not a performance metric that varies if thecandidate software update is loaded onto a more high or low powereddevice.

FIG. 5 is a table showing various metrics and their suitability for usein the regression testing methods disclosed herein. The first column ofthe table identifies the metric, the second column shows whether themetric is a good overall health indicator, the third column showswhether the metric is stable with time, the third column shows whetherthe metric is stable across a mix of user personas or device classes,and the final column shows the overall suitability. In each case, thesuitability of the metric for use in a “before launch” vs. “afterlaunch” comparison of the candidate software update is being considered.

As can be seen, three metrics in rows two to four are evaluated in FIG.5 . The first metric “Message Send” is a count of user messages sentfrom a user's client device 102. The second metric is a ratio of“Message Send” from the user's client device 102 to “Message Send” fromthe messaging server 118. The third metric is a ratio of “Message SendSuccess” from the user's client device 102 to “Message View” at therecipients client device 102.

The “Message Send’ metric is a reasonable indicator of overall healthbut covers only a small portion of the overall functionality ofmessaging system 100 and is not truly representative of overall health.It is also not stable with time, since the volume of messages is likelyto vary significantly depending on the time and type of day. Similarly,it is not particularly stable across different personas or differentdevice classes, since new features may create additional appeal or havea novelty factor, and new features may appeal to new or different groupsof people, with a resultant fluctuation in chatter.

The ratio of “Message Send” from the user's client device 102 to“Message Send” from the messaging server 118 is a reasonable indicatorof overall health of the messaging system 100, since it reflects correctmessage flow, but does not measure the overall user experience. Or,stated differently, this ratio will not give a consistent value if theuser experience is affected by intentional changes that functioncorrectly but that make the user experience better or worse as such.This metric is however likely to be stable with time. As a ratio, itshould be consistent across different times and types of day. However,it may not be stable across different personas or device types, sincenetwork conditions may affect the ratio.

The third metric is a ratio of “Message Send Success” from the user'sclient device 102 to “Message View” at the recipients client device 102.This metric is a good indicator of overall health of the messagingsystem 100 since it captures message flow from sender initiation toviewing by the recipient, and also reflects the social nature of theoverall messaging system 100 product. This metric is also likely to bestable with time. As a ratio, it should be consistent across differenttimes and types of day. It is also likely to be stable across differentpersonas or device types.

FIG. 6 is a flowchart 600 illustrating a regression testing methodaccording to one example. For explanatory purposes, the operations ofthe flowchart 600 are described herein as occurring in serial, orlinearly. However, multiple operations of the flowchart 600 may occur inparallel. In addition, the operations of the flowchart 600 need not beperformed in the order shown and/or one or more blocks of the flowchart600 need not be performed and/or can be replaced by other operations.Furthermore, while the operations in flowchart 600 are described withreference to application launch-support server 130, the associatedfunctionality may alternative be provided as part of a separate softwaredevelopment or software release server system or separated betweendifferent servers or devices, including client devices 102.

The flowchart commences with the application launch-support server 130monitoring one or more operational parameters pre-launch at operation602. This monitoring may be of the relevant parameter(s) on all of theclient devices 102 of current users of the messaging client 104 or onlythose of the intended recipients of the candidate update software ifthose can be specified in advance of the launch. Alternatively, thepre-launch parameters could be extracted after launch from thepre-launch data for the general population, once the client devices 102that receive the candidate software update have been identified. Theparameters could be user engagement parameters, system parameters or anyother operational parameter that can be used to assess the functioningof the candidate update software itself, in the messaging system 100, orwith other systems.

In operation 604, either one or both of the confidence interval 410 andthe prediction interval 412 are determined by the applicationlaunch-support server 130 from the pre-launch parameter data, asdiscussed above with reference to FIG. 3 and FIG. 4 . In the example ofFIG. 6 , a parameter is tested against either a confidence interval or aprediction interval depending on whether or not the parameter has beenassigned to a critical tier. As discussed above, confidence intervalsare used to flag anomalies for metrics that have been assigned to acritical tier while prediction intervals are used for metrics that havebeen assigned to a less critical tier. Accordingly, it is only necessaryto determine the relevant interval (confidence or prediction) inoperation 604 based on whether or not the parameter is or isn't in thecritical tier.

The application launch-support server 130 then receives the candidatesoftware from one or more developer/QA devices 110 in operation 606 andstores it in advance of launch of the update.

The application launch-support server 130 then launches the candidatesoftware update in operation 608. In one example, the candidate softwareupdate is uploaded to an app store as an update to the messaging client104 with the number of available downloads limited to a specific numberof client devices 102. In another example, the candidate software updatecan be pushed to the desired number of client devices 102, which arepreferably chosen randomly or semi-randomly, for example randomly (asfar as practical) within a particular geographical area, time zone,operating system or other system or device parameter that may assist ineliminating a factor that could affect the relevant parameter(s) thatis/are to be monitored for the regression testing.

The application launch-support server 130 then monitors the parameter(s)reflecting the user engagement with the updated messaging client 104 onthe client device 102 in operation 610, and, if necessary, determinessecondary parameters such as ratios and so forth that characterize theuser experience, engagement or functioning of the updated messagingclient as part of the messaging system 100.

Operation 602 and operation 604 may be based on a certain time. In oneexample, user engagement parameters may be monitored for three hoursbefore launch of the update, the candidate software update may beprovided to 700,000 client devices 102 (in a population of millions ofusers) and the engagement parameters may be monitored on the updatedmessaging client 104 for five hours after the update has been launched.These numbers may vary based on any number of considerations, such asthe overall size of the user population and how active each user is onaverage.

As illustrated by decision block 612, at the end of the monitoringperiod, the application launch-support server 130 tests whether or notwhether an observed parameter for the adopters of the candidate softwareupdate is either within the confidence interval or the predictioninterval depending on whether or not the observed parameter is in thecritical tier.

If the parameter is in the critical tier, at operation 614 it isdetermined whether or not the parameter is within an X % (95% in oneexample) confidence interval. If it is, the application launch-supportserver 130 provides a corresponding notification in operation 618, forexample to the developer/QA devices 110 or to a dashboard that thecandidate software update has passed regression testing for thisparameter. If it is not, the method proceeds to operation 620 where theapplication launch-support server 130 provides a correspondingnotification, for example to the developer/QA devices 110 or to adashboard that the candidate software update has failed regressiontesting for this parameter.

If the parameter is not in the critical tier, at operation 616 it isdetermined whether or not the parameter is within an X % (95% in oneexample) prediction interval. If it is, the application launch-supportserver 130 provides a corresponding notification in operation 618, forexample to the developer/QA devices 110 or to a dashboard that thecandidate software update has passed regression testing for thisparameter. If it is not, the method proceeds to operation 620 where theapplication launch-support server 130 provides a correspondingnotification, for example to the developer/QA devices 110 or to adashboard that the candidate software update has failed regressiontesting for this parameter.

FIG. 7 is a flowchart 700 illustrating a regression testing methodaccording to another example. For explanatory purposes, the operationsof the flowchart 700 are described herein as occurring in serial, orlinearly. However, multiple operations of the flowchart 700 may occur inparallel. In addition, the operations of the flowchart 700 need not beperformed in the order shown and/or one or more blocks of the flowchart700 need not be performed and/or can be replaced by other operations.Furthermore, while the operations in flowchart 700 are described withreference to application launch-support server 130, the associatedfunctionality may alternative be provided as part of a separate softwaredevelopment or software release server system or separated betweendifferent servers or devices, including client devices 102.

The flowchart commences with the application launch-support server 130monitoring one or more operational parameters pre-launch at operation702. This monitoring may be of the relevant parameter(s) on all of theclient devices 102 of current users of the messaging client 104 or onlythose of the intended recipients of the candidate update software ifthose can be specified in advance of the launch. Alternatively, thepre-launch parameters could be extracted after launch from thepre-launch data for the general population, once the client devices 102that receive the candidate software update have been identified. Theparameters could be user engagement parameters, system parameters or anyother operational parameter that can be used to assess the functioningof the candidate update software itself, in the messaging system 100, orwith other systems.

In operation 704, the confidence interval 410 and the predictioninterval 412 are determined by the application launch-support server 130from the pre-launch parameter data, as discussed above with reference toFIG. 3 and FIG. 4 . The application launch-support server 130 thenreceives the candidate software from one or more developer/QA devices110 in operation 706 and stores it in advance of launch of the update.

The application launch-support server 130 then launches the candidatesoftware update in operation 708. In one example, the candidate softwareupdate is uploaded to an app store as an update to the messaging client104 with the number of available downloads limited to a specific numberof client devices 102. In another example, the candidate software updatecan be pushed to the desired number of client devices 102, which arepreferably chosen randomly or semi-randomly, for example randomly (asfar as practical) within a particular geographical area, time zone,operating system or other system or device parameter that may assist ineliminating a factor that could affect the relevant parameter(s) thatis/are to be monitored for the regression testing.

The application launch-support server 130 then monitors the parameter(s)reflecting the user engagement with the updated messaging client 104 onthe client device 102 in operation 710, and, if necessary, determinessecondary parameters such as ratios and so forth that characterize theuser experience, engagement or functioning of the updated messagingclient as part of the messaging system 100.

Operation 702 and operation 704 may be based on a certain time. In oneexample, user engagement parameters may be monitored for three hoursbefore launch of the update, the candidate software update may beprovided to 800,000 client devices 102 (in a population of millions ofusers) and the engagement parameters may be monitored on the updatedmessaging client 104 for five hours after the update has been launched.These numbers may vary based on any number of considerations, such asthe overall size of the user population and how active each user is onaverage.

At operation 712, at the end of the monitoring period, the applicationlaunch-support server 130 determines in operation 712 whether anobserved parameter for the adopters of the candidate software update iswithin an X % (95% in one example) confidence interval. If it is, theapplication launch-support server 130 provides a correspondingnotification in operation 714, for example to the developer/QA devices110 or to a dashboard that the candidate software update has passedregression testing for this parameter. If it is not, the method proceedsto operation 716.

At operation 716, the application launch-support server 130 determinesin whether the observed parameter for the adopters of the candidatesoftware update is within an X % (95% in one example) predictioninterval. If it is, the application launch-support server 130 provides acorresponding notification in operation 718, for example to thedeveloper/QA devices 110 or to a dashboard interface, that the candidatesoftware update and the observed parameters require review to determinewhether or not the candidate software update is functioning correctly.In this case, the observed parameter is outside the confidence intervalbut still within the prediction interval.

If the application launch-support server 130 determines in operation 716that the observed parameter for the adopters of the candidate softwareupdate is outside the X % (95% in one example) prediction interval, theapplication launch-support server 130 provides a notification inoperation 720, for example to the developer/QA devices 110 or to adashboard interface, that the candidate software update is notfunctioning correctly.

Machine Architecture

FIG. 8 is a diagrammatic representation of the machine 800 within whichinstructions 810 (e.g., software, a program, an application, an applet,an app, or other executable code) for causing the machine 800 to performany one or more of the methodologies discussed herein may be executed.For example, the instructions 810 may cause the machine 800 to executeany one or more of the methods described herein. The instructions 810transform the general, non-programmed machine 800 into a particularmachine 800 programmed to carry out the described and illustratedfunctions in the manner described. The machine 800 may operate as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 800 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 800 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smartphone, a mobile device, a wearable device(e.g., a smartwatch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 810, sequentially or otherwise, that specify actions to betaken by the machine 800. Further, while only a single machine 800 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 810 to perform any one or more of the methodologiesdiscussed herein. The machine 800, for example, may comprise the clientdevice 102 or any one of a number of server devices forming part of themessaging server system 108. In some examples, the machine 800 may alsocomprise both client and server systems, with certain operations of aparticular method or algorithm being performed on the server-side andwith certain operations of the particular method or algorithm beingperformed on the client-side.

The machine 800 may include processors 804, memory 806, and input/outputI/O components 802, which may be configured to communicate with eachother via a bus 840. In an example, the processors 804 (e.g., a CentralProcessing Unit (CPU), a Reduced Instruction Set Computing (RISC)Processor, a Complex Instruction Set Computing (CISC) Processor, aGraphics Processing Unit (GPU), a Digital Signal Processor (DSP), anApplication Specific Integrated Circuit (ASIC), a Radio-FrequencyIntegrated Circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 808 and aprocessor 812 that execute the instructions 810. The term “processor” isintended to include multi-core processors that may comprise two or moreindependent processors (sometimes referred to as “cores”) that mayexecute instructions contemporaneously. Although FIG. 8 shows multipleprocessors 804, the machine 800 may include a single processor with asingle-core, a single processor with multiple cores (e.g., a multi-coreprocessor), multiple processors with a single core, multiple processorswith multiples cores, or any combination thereof.

The memory 806 includes a main memory 814, a static memory 816, and astorage unit 818, both accessible to the processors 804 via the bus 840.The main memory 806, the static memory 816, and storage unit 818 storethe instructions 810 embodying any one or more of the methodologies orfunctions described herein. The instructions 810 may also reside,completely or partially, within the main memory 814, within the staticmemory 816, within machine-readable medium 820 within the storage unit818, within at least one of the processors 804 (e.g., within theProcessor's cache memory), or any suitable combination thereof, duringexecution thereof by the machine 800.

The I/O components 802 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 802 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 802 mayinclude many other components that are not shown in FIG. 8 . In variousexamples, the I/O components 802 may include user output components 826and user input components 828. The user output components 826 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light-emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The userinput components 828 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and force of touches or touch gestures, or other tactile inputcomponents), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 802 may include biometriccomponents 830, motion components 832, environmental components 834, orposition components 836, among a wide array of other components. Forexample, the biometric components 830 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye-tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 832 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope).

The environmental components 834 include, for example, one or cameras(with still image/photograph and video capabilities), illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment.

With respect to cameras, the client device 102 may have a camera systemcomprising, for example, front cameras on a front surface of the clientdevice 102 and rear cameras on a rear surface of the client device 102.The front cameras may, for example, be used to capture still images andvideo of a user of the client device 102 (e.g., “selfies”), which maythen be augmented with augmentation data (e.g., filters) describedabove. The rear cameras may, for example, be used to capture stillimages and videos in a more traditional camera mode, with these imagessimilarly being augmented with augmentation data. In addition to frontand rear cameras, the client device 102 may also include a 360° camerafor capturing 360° photographs and videos.

Further, the camera system of a client device 102 may include dual rearcameras (e.g., a primary camera as well as a depth-sensing camera), oreven triple, quad or penta rear camera configurations on the front andrear sides of the client device 102. These multiple cameras systems mayinclude a wide camera, an ultra-wide camera, a telephoto camera, a macrocamera and a depth sensor, for example.

The position components 836 include location sensor components (e.g., aGPS receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 802 further include communication components 838operable to couple the machine 800 to a network 822 or devices 824 viarespective coupling or connections. For example, the communicationcomponents 838 may include a network interface Component or anothersuitable device to interface with the network 822. In further examples,the communication components 838 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 824 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 838 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 838 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components838, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., main memory 814, static memory 816, andmemory of the processors 804) and storage unit 818 may store one or moresets of instructions and data structures (e.g., software) embodying orused by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 810), when executedby processors 804, cause various operations to implement the disclosedexamples.

The instructions 810 may be transmitted or received over the network822, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components838) and using any one of several well-known transfer protocols (e.g.,hypertext transfer protocol (HTTP)). Similarly, the instructions 810 maybe transmitted or received using a transmission medium via a coupling(e.g., a peer-to-peer coupling) to the devices 824.

Software Architecture

FIG. 9 is a block diagram 900 illustrating a software architecture 904,which can be installed on any one or more of the devices describedherein. The software architecture 904 is supported by hardware such as amachine 902 that includes processors 920, memory 926, and I/O components938. In this example, the software architecture 904 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 904 includes layerssuch as an operating system 912, libraries 910, frameworks 908, andapplications 906. Operationally, the applications 906 invoke API calls950 through the software stack and receive messages 952 in response tothe API calls 950.

The operating system 912 manages hardware resources and provides commonservices. The operating system 912 includes, for example, a kernel 914,services 916, and drivers 922. The kernel 914 acts as an abstractionlayer between the hardware and the other software layers. For example,the kernel 914 provides memory management, processor management (e.g.,scheduling), component management, networking, and security settings,among other functionality. The services 916 can provide other commonservices for the other software layers. The drivers 922 are responsiblefor controlling or interfacing with the underlying hardware. Forinstance, the drivers 922 can include display drivers, camera drivers,BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers,serial communication drivers (e.g., USB drivers), WI-FI® drivers, audiodrivers, power management drivers, and so forth.

The libraries 910 provide a common low-level infrastructure used by theapplications 906. The libraries 910 can include system libraries 918(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 910 can include APIlibraries 924 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries (e.g., SQLite to provide various relational databasefunctions), web libraries (e.g., WebKit to provide web browsingfunctionality), and the like. The libraries 910 can also include a widevariety of other libraries 928 to provide many other APIs to theapplications 906.

The frameworks 908 provide a common high-level infrastructure that isused by the applications 906. For example, the frameworks 908 providevarious graphical user interface (GUI) functions, high-level resourcemanagement, and high-level location services. The frameworks 908 canprovide a broad spectrum of other APIs that can be used by theapplications 906, some of which may be specific to a particularoperating system or platform.

In an example, the applications 906 may include a home application 936,a contacts application 930, a browser application 932, a book readerapplication 934, a location application 942, a media application 944, amessaging application 946, a game application 948, and a broadassortment of other applications such as a third-party application 940.The applications 906 are programs that execute functions defined in theprograms. Various programming languages can be employed to create one ormore of the applications 906, structured in a variety of manners, suchas object-oriented programming languages (e.g., Objective-C, Java, orC++) or procedural programming languages (e.g., C or assembly language).In a specific example, the third-party application 940 (e.g., anapplication developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform)may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. Inthis example, the third-party application 940 can invoke the API calls950 provided by the operating system 912 to facilitate functionalitydescribed herein.

Glossary

“Carrier signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Client device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops,multi-processor systems, microprocessor-based or programmable consumerelectronics, game consoles, set-top boxes, or any other communicationdevice that a user may use to access a network.

“Communication network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In variousexamples, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor or agroup of processors) may be configured by software (e.g., an applicationor application portion) as a hardware component that operates to performcertain operations as described herein. A hardware component may also beimplemented mechanically, electronically, or any suitable combinationthereof. For example, a hardware component may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware component may be a special-purpose processor,such as a field-programmable gate array (FPGA) or an applicationspecific integrated circuit (ASIC). A hardware component may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software), may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering examples in which hardwarecomponents are temporarily configured (e.g., programmed), each of thehardware components need not be configured or instantiated at any oneinstance in time. For example, where a hardware component comprises ageneral-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware component at one instanceof time and to constitute a different hardware component at a differentinstance of time. Hardware components can provide information to, andreceive information from, other hardware components. Accordingly, thedescribed hardware components may be regarded as being communicativelycoupled. Where multiple hardware components exist contemporaneously,communications may be achieved through signal transmission (e.g., overappropriate circuits and buses) between or among two or more of thehardware components. In examples in which multiple hardware componentsare configured or instantiated at different times, communicationsbetween such hardware components may be achieved, for example, throughthe storage and retrieval of information in memory structures to whichthe multiple hardware components have access. For example, one hardwarecomponent may perform an operation and store the output of thatoperation in a memory device to which it is communicatively coupled. Afurther hardware component may then, at a later time, access the memorydevice to retrieve and process the stored output. Hardware componentsmay also initiate communications with input or output devices, and canoperate on a resource (e.g., a collection of information). The variousoperations of example methods described herein may be performed, atleast partially, by one or more processors that are temporarilyconfigured (e.g., by software) or permanently configured to perform therelevant operations. Whether temporarily or permanently configured, suchprocessors may constitute processor-implemented components that operateto perform one or more operations or functions described herein. As usedherein, “processor-implemented component” refers to a hardware componentimplemented using one or more processors. Similarly, the methodsdescribed herein may be at least partially processor-implemented, with aparticular processor or processors being an example of hardware. Forexample, at least some of the operations of a method may be performed byone or more processors or processor-implemented components. Moreover,the one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), with these operations being accessiblevia a network (e.g., the Internet) and via one or more appropriateinterfaces (e.g., an API). The performance of certain of the operationsmay be distributed among the processors, not only residing within asingle machine, but deployed across a number of machines. In someexamples, the processors or processor-implemented components may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In otherexamples, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“Computer-readable storage medium” refers to both machine-storage mediaand transmission media. Thus, the terms include both storagedevices/media and carrier waves/modulated data signals. The terms“machine-readable medium,” “computer-readable medium” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure.

“Ephemeral message” refers to a message that is accessible for atime-limited duration. An ephemeral message may be a text, an image, avideo and the like. The access time for the ephemeral message may be setby the message sender. Alternatively, the access time may be a defaultsetting or a setting specified by the recipient. Regardless of thesetting technique, the message is transitory.

“Machine storage medium” refers to a single or multiple storage devicesand media (e.g., a centralized or distributed database, and associatedcaches and servers) that store executable instructions, routines anddata. The term shall accordingly be taken to include, but not be limitedto, solid-state memories, and optical and magnetic media, includingmemory internal or external to processors. Specific examples ofmachine-storage media, computer-storage media and device-storage mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), FPGA, andflash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks Theterms “machine-storage medium,” “device-storage medium,”“computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangiblemedium that is capable of storing, encoding, or carrying theinstructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

What is claimed is:
 1. A method of software launch regression testing,performed by one or more processors, comprising: determining a currentvalue of a user engagement parameter of an existing application fromdata received from a plurality of client devices running the existingapplication; determining a probability interval for the user engagementparameter of the existing application from the data received from theplurality of client devices running the existing application; launchinga candidate update application to client devices comprising a subset ofthe plurality of client devices; determining a value of the userengagement parameter using data received from of the subset of theplurality of client devices running the candidate update application;determining if the value of the user engagement parameter of thecandidate update application falls within the probability interval; andbased on the value of the user engagement parameter falling within theprobability interval, providing a testing pass notification.
 2. Themethod of claim 1, wherein a size of the probability interval depends onwhether or not the user engagement parameter is a critical parameter. 3.The method of claim 1, wherein the probability interval comprises aconfidence interval within which correct functioning of the candidateupdate application is assumed, the method further comprising:determining a prediction interval from the data received from theplurality of client devices running the existing application, theprediction interval comprising an interval of values of the userengagement parameter within which the candidate update application mayor may not be functioning correctly; determining if the value of thevalue of the user engagement parameter of the candidate updateapplication falls within the prediction interval; and based on the valueof the value of the user engagement parameter falling outside theconfidence interval but within the prediction interval, providing atesting review notification.
 4. The method of claim 3, furthercomprising: based on the value of the user engagement parameter fallingoutside the prediction interval, providing a testing fail notification.5. The method of claim 1, wherein launching the candidate updateapplication comprises: uploading the candidate update application to anapp store, wherein a number of available downloads of the candidateupdate application from the app store is limited to a predeterminednumber.
 6. The method of claim 1, wherein launching the candidate updateapplication comprises: limiting launch of the candidate updateapplication to client devices having a particular characteristic.
 7. Themethod of claim 6, wherein the particular characteristic is location ofthe client devices within a particular geographical area or time zone.8. The method of claim 1, wherein the user engagement parameter is aratio of transmissions of content from the subset of the plurality ofclient devices to successful viewing of the content on recipient clientdevices.
 9. A non-transitory computer-readable storage medium, thecomputer-readable storage medium including instructions that whenexecuted by a computer, cause the computer to perform operations forsoftware launch regression testing, comprising: determining a currentvalue of a user engagement parameter of an existing application fromdata received from a plurality of client devices running the existingapplication; determining a probability interval for the user engagementparameter of the existing application from the data received from theplurality of client devices running the existing application; launchinga candidate update application to client devices comprising a subset ofthe plurality of client devices; determining a value of the userengagement parameter using data received from of the subset of theplurality of client devices running the candidate update application;determining if the value of the user engagement parameter of thecandidate update application falls within the probability interval; andbased on the value of the user engagement parameter falling within theprobability interval, providing a testing pass notification.
 10. Thecomputer-readable storage medium of claim 9, wherein a size of theprobability interval depends on whether or not the user engagementparameter is a critical parameter.
 11. The computer-readable storagemedium of claim 9, wherein the probability interval comprises aconfidence interval within which correct functioning of the candidateupdate application is assumed, wherein the instructions furtherconfigure the computer to perform operations further comprising:determining a prediction interval from the data received from theplurality of client devices running the existing application, theprediction interval comprising an interval of values of the userengagement parameter within which the candidate update application mayor may not be functioning correctly; determining if the value of thevalue of the user engagement parameter of the candidate updateapplication falls within the prediction interval; and based on the valueof the value of the user engagement parameter falling outside theconfidence interval but within the prediction interval, providing atesting review notification.
 12. The computer-readable storage medium ofclaim 11, wherein the instructions further configure the computer toperform operations further comprising: based on the value of the userengagement parameter falling outside the prediction interval, providinga testing fail notification.
 13. The computer-readable storage medium ofclaim 9, wherein launching the candidate update application comprises:uploading the candidate update application to an app store, wherein anumber of available downloads of the candidate update application fromthe app store is limited to a predetermined number.
 14. Thecomputer-readable storage medium of claim 9, wherein the user engagementparameter is a ratio of transmissions of content from the subset of theplurality of client devices to successful viewing of the content onrecipient client devices.
 15. A computing apparatus comprising: aprocessor; and a memory storing instructions that, when executed by theprocessor, configure the apparatus to perform operations comprising:determining a current value of a user engagement parameter of anexisting application from data received from a plurality of clientdevices running the existing application; determining a probabilityinterval for the user engagement parameter of the existing applicationfrom the data received from the plurality of client devices running theexisting application; launching a candidate update application to clientdevices comprising a subset of the plurality of client devices;determining a value of the user engagement parameter using data receivedfrom of the subset of the plurality of client devices running thecandidate update application; determining if the value of the userengagement parameter of the candidate update application falls withinthe probability interval; and based on the value of the user engagementparameter falling within the probability interval, providing a testingpass notification.
 16. The computing apparatus of claim 15, wherein asize of the probability interval depends on whether or not the userengagement parameter is a critical parameter.
 17. The computingapparatus of claim 15, wherein the probability interval comprises aconfidence interval within which correct functioning of the candidateupdate application is assumed, wherein the operations further comprise:determining a prediction interval from the data received from theplurality of client devices running the existing application, theprediction interval comprising an interval of values of the userengagement parameter within which the candidate update application mayor may not be functioning correctly; determining if the value of thevalue of the user engagement parameter of the candidate updateapplication falls within the prediction interval; and based on the valueof the value of the user engagement parameter falling outside theconfidence interval but within the prediction interval, providing atesting review notification.
 18. The computing apparatus of claim 17,wherein the operations further comprise: based on the value of the userengagement parameter falling outside the prediction interval, providinga testing fail notification.
 19. The computing apparatus of claim 15,wherein launching of the candidate update application comprises:uploading the candidate update application to an app store, wherein anumber of available downloads of the candidate update application fromthe app store is limited to a predetermined number.
 20. The computingapparatus of claim 15, wherein the user engagement parameter is a ratioof transmissions of content from the subset of the plurality of clientdevices to successful viewing of the content on recipient clientdevices.